报告题目：

Accurate and efficient numerical methods for molecular dynamics and data science using adaptive thermostats

报 告 人：

商晓成 博士（瑞士苏黎世联邦理工）

报告时间：

2018年04月21日 16:00-16:40

报告地点：

理学院东北楼四楼报告厅（404）

报告摘要：

I will discuss the design of state-of-the-art numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. Extended stochastic dynamical methods, known as adaptive thermostats that automatically correct thermodynamic averages using a negative feedback loop, are discussed which have application to molecular dynamics and Bayesian sampling techniques arising in emerging machine learning applications. I will also discuss the characteristics of different algorithms, including the convergence of averages and the accuracy of numerical discretizations.